Capacity gain of mixed multicast/unicast transport schemes in a TV distribution network

  • Authors:

  • Zlatka Avramova;Danny De Vleeschauwer;Sabine Wittevrongel;Herwig Bruneel

  • Affiliations:

  • Stochastic Modeling and Analysis of Communication Systems Research Group, Department of Telecommunications and Information Processing, Faculty of Engineering, Ghent University, Ghent, Belgium;Bell Labs, Alcatel-Lucent Bell, Antwerp, Belgium;Stochastic Modeling and Analysis of Communication Systems Research Group, Department of Telecommunications and Information Processing, Faculty of Engineering, Ghent University, Ghent, Belgium;Stochastic Modeling and Analysis of Communication Systems Research Group, Department of Telecommunications and Information Processing, Faculty of Engineering, Ghent University, Ghent, Belgium

  • Venue:
  • IEEE Transactions on Multimedia
  • Year:
  • 2009

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Abstract

This paper presents three approaches to estimate the required resources in an infrastructure where digital TV channels can be delivered in unicast or multicast (broadcast) mode. Such situations arise for example in Cable TV, IPTV distribution networks or in (future) hybrid mobile TV networks. The three approaches presented are an exact calculation, a Gaussian approximation and a simulation tool. We investigate two scenarios that allow saving bandwidth resources. In a static scenario, the most popular channels are multicast and the less popular channels rely on unicast. In a dynamic scenario, the list of multicast channels is dynamic and governed by the users' behavior. We prove that the dynamic scenario always outperforms the static scenario. We demonstrate the robustness, versatility and the limits of our three approaches. The exact calculation application is limited because it is computationally expensive for cases with large numbers of users and channels, while the Gaussian approximation is good exactly for such systems. The simulation tool takes long to yield results for small blocking probabilities. We explore the capacity gain regions under varying model parameters. Finally, we illustrate our methods by discussing some realistic network scenarios using channel popularities based on measurement data as much as possible.